This series of files compile all analyses done during Chapter 2:

All analyses have been done with R 3.6.3.

Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it

To assess Section 1, click here.
To go back to the summary page, click here.

Sources of activity considered for the analyses:

Fisheries data considered for the analyses (expressed as number of fishing events or kilograms of collected individuals for each gear):

Gear Code Years Events Species
Trap TrapFish 2010-2015 1061 Buccinum sp., Cancer irroratus, Chionoecetes opilio, Homarus americanus
Bottom-trawl TrawFish 2013-2014 2 Pandalus borealis
Net NetFish 2010 5 Clupea harengus, Gadus morhua
Dredge DredFish 2010-2014 21 Mactromeris polynyma

1. Explorations

1.1. Relationships between parameters

This section explores relationships between each pair of parameters or AH distances.

Fist, we can compute the Spearman’s correlation between each parameter.

Correlation coefficients between habitat parameters and metals concentrations
  om gravel sand silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc S N H J aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge cumulative_influence
om 1 -0.379 -0.708 0.68 0.023 0.57 0.486 0.606 0.593 0.586 0.568 0.537 0.587 0.605 -0.026 -0.121 0.122 0.136 -0.41 -0.033 0.22 0.43 0.309 0.518 0.291 0.239 0.401 -0.073 0.366 -0.508 -0.213 0.004 -0.238 0.274
gravel -0.379 1 0.186 -0.471 0.099 -0.127 -0.23 -0.194 -0.23 -0.232 -0.199 -0.207 -0.169 -0.235 0.029 0.007 0.012 0.07 0.143 -0.068 -0.175 -0.161 -0.081 -0.216 -0.02 0 -0.073 -0.061 -0.166 0.16 0.172 -0.055 0.068 -0.12
sand -0.708 0.186 1 -0.788 -0.308 -0.648 -0.504 -0.587 -0.483 -0.407 -0.524 -0.611 -0.588 -0.529 0.059 0.084 0.031 -0.027 0.433 0.279 -0.002 -0.17 -0.102 -0.42 -0.169 -0.31 -0.468 0.324 -0.17 0.428 0.087 -0.158 0.246 -0.058
silt 0.68 -0.471 -0.788 1 0.05 0.548 0.482 0.506 0.441 0.362 0.449 0.566 0.519 0.493 -0.054 -0.013 -0.068 -0.055 -0.409 -0.15 0.046 0.179 0.079 0.424 0.083 0.247 0.391 -0.196 0.175 -0.385 -0.18 0.119 -0.241 0.076
clay 0.023 0.099 -0.308 0.05 1 0.208 0.095 0.129 0.11 0.062 0.144 0.052 0.15 0.107 -0.098 -0.076 -0.044 0.02 -0.059 -0.02 0.025 0.015 0.02 0.006 0.012 0.241 0.238 -0.031 0.037 -0.096 -0.104 0.191 -0.046 0.066
arsenic 0.57 -0.127 -0.648 0.548 0.208 1 0.797 0.862 0.791 0.656 0.78 0.707 0.902 0.87 -0.266 -0.149 -0.193 0.008 -0.657 -0.067 0.06 0.216 0.076 0.66 0.214 0.522 0.709 -0.158 0.213 -0.444 -0.237 0.078 -0.466 0.181
cadmium 0.486 -0.23 -0.504 0.482 0.095 0.797 1 0.818 0.694 0.564 0.717 0.75 0.886 0.853 -0.308 -0.042 -0.291 -0.133 -0.783 -0.015 -0.002 0.186 -0.084 0.728 0.039 0.468 0.701 -0.14 0.142 -0.345 -0.306 -0.001 -0.46 0.111
chromium 0.606 -0.194 -0.587 0.506 0.129 0.862 0.818 1 0.909 0.818 0.917 0.753 0.92 0.939 -0.331 -0.167 -0.273 -0.041 -0.73 -0.02 0.263 0.437 0.103 0.733 0.344 0.596 0.763 -0.103 0.409 -0.323 -0.349 0.055 -0.559 0.362
copper 0.593 -0.23 -0.483 0.441 0.11 0.791 0.694 0.909 1 0.808 0.842 0.715 0.872 0.952 -0.298 -0.172 -0.225 -0.025 -0.655 0.238 0.438 0.571 0.219 0.752 0.432 0.641 0.755 0.169 0.536 -0.319 -0.451 0.033 -0.602 0.553
iron 0.586 -0.232 -0.407 0.362 0.062 0.656 0.564 0.818 0.808 1 0.878 0.461 0.664 0.785 -0.377 -0.273 -0.251 0.034 -0.616 0.114 0.508 0.652 0.325 0.671 0.624 0.578 0.674 0.061 0.644 -0.292 -0.368 0.055 -0.648 0.596
manganese 0.568 -0.199 -0.524 0.449 0.144 0.78 0.717 0.917 0.842 0.878 1 0.658 0.798 0.846 -0.287 -0.096 -0.261 -0.085 -0.758 -0.02 0.422 0.557 0.209 0.769 0.497 0.671 0.822 -0.082 0.562 -0.301 -0.466 0.106 -0.649 0.493
mercury 0.537 -0.207 -0.611 0.566 0.052 0.707 0.75 0.753 0.715 0.461 0.658 1 0.845 0.755 -0.234 -0.084 -0.199 -0.075 -0.724 -0.064 0.053 0.237 -0.043 0.731 0.133 0.509 0.703 -0.142 0.196 -0.356 -0.314 0.01 -0.42 0.168
lead 0.587 -0.169 -0.588 0.519 0.15 0.902 0.886 0.92 0.872 0.664 0.798 0.845 1 0.939 -0.304 -0.135 -0.252 -0.051 -0.707 0.029 0.129 0.316 0.05 0.739 0.211 0.599 0.769 -0.065 0.277 -0.376 -0.307 0.036 -0.475 0.275
zinc 0.605 -0.235 -0.529 0.493 0.107 0.87 0.853 0.939 0.952 0.785 0.846 0.755 0.939 1 -0.32 -0.145 -0.253 -0.056 -0.718 0.152 0.307 0.471 0.151 0.791 0.341 0.603 0.764 0.067 0.429 -0.359 -0.397 0.026 -0.578 0.431
S -0.026 0.029 0.059 -0.054 -0.098 -0.266 -0.308 -0.331 -0.298 -0.377 -0.287 -0.234 -0.304 -0.32 1 0.561 0.705 -0.052 0.348 -0.141 -0.113 -0.158 0.077 -0.255 -0.045 -0.315 -0.33 -0.069 -0.151 0.079 0.217 -0.112 0.335 -0.164
N -0.121 0.007 0.084 -0.013 -0.076 -0.149 -0.042 -0.167 -0.172 -0.273 -0.096 -0.084 -0.135 -0.145 0.561 1 -0.041 -0.683 -0.005 -0.061 -0.139 -0.163 -0.169 -0.023 -0.111 -0.116 -0.067 -0.061 -0.169 0.183 -0.008 -0.137 0.029 -0.133
H 0.122 0.012 0.031 -0.068 -0.044 -0.193 -0.291 -0.273 -0.225 -0.251 -0.261 -0.199 -0.252 -0.253 0.705 -0.041 1 0.598 0.373 -0.087 0.026 -0.014 0.285 -0.243 -0.028 -0.306 -0.328 -0.004 -0.015 -0.058 0.163 -0.035 0.425 -0.089
J 0.136 0.07 -0.027 -0.055 0.02 0.008 -0.133 -0.041 -0.025 0.034 -0.085 -0.075 -0.051 -0.056 -0.052 -0.683 0.598 1 0.186 0.01 0.073 0.054 0.236 -0.128 -0.021 -0.105 -0.146 0.034 0.061 -0.168 0.033 0.127 0.23 -0.014
aquaculture -0.41 0.143 0.433 -0.409 -0.059 -0.657 -0.783 -0.73 -0.655 -0.616 -0.758 -0.724 -0.707 -0.718 0.348 -0.005 0.373 0.186 1 -0.069 -0.168 -0.306 0.128 -0.827 -0.183 -0.597 -0.839 0.079 -0.271 0.282 0.594 0.02 0.722 -0.233
city -0.033 -0.068 0.279 -0.15 -0.02 -0.067 -0.015 -0.02 0.238 0.114 -0.02 -0.064 0.029 0.152 -0.141 -0.061 -0.087 0.01 -0.069 1 0.292 0.369 0.051 0.299 0.133 0.324 0.162 0.969 0.255 -0.097 -0.401 -0.012 -0.424 0.564
dredging_collect 0.22 -0.175 -0.002 0.046 0.025 0.06 -0.002 0.263 0.438 0.508 0.422 0.053 0.129 0.307 -0.113 -0.139 0.026 0.073 -0.168 0.292 1 0.895 0.734 0.359 0.741 0.485 0.416 0.371 0.946 0.012 -0.507 0.028 -0.366 0.849
dredging_dump 0.43 -0.161 -0.17 0.179 0.015 0.216 0.186 0.437 0.571 0.652 0.557 0.237 0.316 0.471 -0.158 -0.163 -0.014 0.054 -0.306 0.369 0.895 1 0.723 0.588 0.805 0.545 0.521 0.436 0.956 -0.173 -0.465 -0.048 -0.518 0.924
industry 0.309 -0.081 -0.102 0.079 0.02 0.076 -0.084 0.103 0.219 0.325 0.209 -0.043 0.05 0.151 0.077 -0.169 0.285 0.236 0.128 0.051 0.734 0.723 1 0.183 0.656 0.17 0.146 0.17 0.765 -0.172 -0.09 -0.086 -0.041 0.61
shipping_mooring 0.518 -0.216 -0.42 0.424 0.006 0.66 0.728 0.733 0.752 0.671 0.769 0.731 0.739 0.791 -0.255 -0.023 -0.243 -0.128 -0.827 0.299 0.359 0.588 0.183 1 0.41 0.592 0.766 0.24 0.5 -0.417 -0.597 -0.05 -0.728 0.543
shipping_traffic 0.291 -0.02 -0.169 0.083 0.012 0.214 0.039 0.344 0.432 0.624 0.497 0.133 0.211 0.341 -0.045 -0.111 -0.028 -0.021 -0.183 0.133 0.741 0.805 0.656 0.41 1 0.568 0.451 0.232 0.834 -0.005 -0.181 0.097 -0.521 0.825
sewers_rain 0.239 0 -0.31 0.247 0.241 0.522 0.468 0.596 0.641 0.578 0.671 0.509 0.599 0.603 -0.315 -0.116 -0.306 -0.105 -0.597 0.324 0.485 0.545 0.17 0.592 0.568 1 0.892 0.279 0.571 -0.07 -0.509 0.209 -0.747 0.661
sewers_waste 0.401 -0.073 -0.468 0.391 0.238 0.709 0.701 0.763 0.755 0.674 0.822 0.703 0.769 0.764 -0.33 -0.067 -0.328 -0.146 -0.839 0.162 0.416 0.521 0.146 0.766 0.451 0.892 1 0.069 0.523 -0.257 -0.569 0.061 -0.75 0.522
wharves_city -0.073 -0.061 0.324 -0.196 -0.031 -0.158 -0.14 -0.103 0.169 0.061 -0.082 -0.142 -0.065 0.067 -0.069 -0.061 -0.004 0.034 0.079 0.969 0.371 0.436 0.17 0.24 0.232 0.279 0.069 1 0.331 -0.042 -0.34 0.016 -0.35 0.632
wharves_industry 0.366 -0.166 -0.17 0.175 0.037 0.213 0.142 0.409 0.536 0.644 0.562 0.196 0.277 0.429 -0.151 -0.169 -0.015 0.061 -0.271 0.255 0.946 0.956 0.765 0.5 0.834 0.571 0.523 0.331 1 -0.094 -0.454 0.037 -0.497 0.9
fisheries_trap -0.508 0.16 0.428 -0.385 -0.096 -0.444 -0.345 -0.323 -0.319 -0.292 -0.301 -0.356 -0.376 -0.359 0.079 0.183 -0.058 -0.168 0.282 -0.097 0.012 -0.173 -0.172 -0.417 -0.005 -0.07 -0.257 -0.042 -0.094 1 0.226 0.132 0.145 -0.075
fisheries_trawl -0.213 0.172 0.087 -0.18 -0.104 -0.237 -0.306 -0.349 -0.451 -0.368 -0.466 -0.314 -0.307 -0.397 0.217 -0.008 0.163 0.033 0.594 -0.401 -0.507 -0.465 -0.09 -0.597 -0.181 -0.509 -0.569 -0.34 -0.454 0.226 1 -0.071 0.513 -0.458
fisheries_net 0.004 -0.055 -0.158 0.119 0.191 0.078 -0.001 0.055 0.033 0.055 0.106 0.01 0.036 0.026 -0.112 -0.137 -0.035 0.127 0.02 -0.012 0.028 -0.048 -0.086 -0.05 0.097 0.209 0.061 0.016 0.037 0.132 -0.071 1 -0.08 0.077
fisheries_dredge -0.238 0.068 0.246 -0.241 -0.046 -0.466 -0.46 -0.559 -0.602 -0.648 -0.649 -0.42 -0.475 -0.578 0.335 0.029 0.425 0.23 0.722 -0.424 -0.366 -0.518 -0.041 -0.728 -0.521 -0.747 -0.75 -0.35 -0.497 0.145 0.513 -0.08 1 -0.617
cumulative_influence 0.274 -0.12 -0.058 0.076 0.066 0.181 0.111 0.362 0.553 0.596 0.493 0.168 0.275 0.431 -0.164 -0.133 -0.089 -0.014 -0.233 0.564 0.849 0.924 0.61 0.543 0.825 0.661 0.522 0.632 0.9 -0.075 -0.458 0.077 -0.617 1

For the regressions, several types of models were considered: linear, quadratic, exponential and logarithmic. Only linear and quadratic models were implemented as there are some bugs with the calculation of the others. The model with the highest \(R^{2}\) is presented on each plot.

AquaInf

CityInf

InduInf

CollDred

DumpDred

MoorShip

TrafShip

RainSew

WastSew

CityWha

InduWha

TrapFish

TrawFish

NetFish

DredFish

Cumulative Influence

1.2. Species abundances by cumulative influence group

Phylum abundances by group
Phylum low bad moderate high good
Annelida NA 29.5 32.3 19.7 NA
Arthropoda NA 20.3 50.6 26.3 NA
Cnidaria NA 0.1 0 0 NA
Echinodermata NA 13.5 1.32 5.12 NA
Mollusca NA 10.8 13.5 15.9 NA
Nematoda NA 22.7 9.95 0.0625 NA
Nemertea NA 0 0.195 0 NA
Sipuncula NA 0 0.268 0.438 NA

2. Hierarchical Modelling of Species Communities

We will use the probabilities and indices of influences calculated in Section 1 here. The aim is to obtain predictive models for the benthic communities, based on the abiotic parameters and the human activities.

HMSC models have been developped in a dedicated script, and the R workspace has been imported here.

First, we initiate the HMSC model with the chosen data, priors and parameters.

HMSC_model <- hmsc(HMSC_data, HMSC_param, HMSC_prior, family = "overPoisson", niter = 100000, nburn = 1000, thin = 100)

Here are the diagnostics to evaluate each model’s quality.

Human activities

Trace plots

Explanatory power

Confidence intervals

Variance partitioning

Habitat parameters

Trace plots

Explanatory power

Confidence intervals

Variance partitioning

All variables

Trace plots

Explanatory power

Confidence intervals

Variance partitioning

Finally, we can predict the values of our parameters within BSI.